The goal of the proposed research is to develop a 12three-dimensional massive-training artificial neural network (3D MTANN) for a computer-aided diagnostic (CAD) scheme for detection of colorectal polyps in computed tomographic colonography (CTC). The CAD output will be used as a "second opinion" to assist radiologists in detecting polyps for early detection of colorectal cancer. We will develop a CAD scheme incorporating a 3D MTANN for distinction between polyps and non-polyps (false positives) to reduce the number of false positives as much as possible, while maintaining a high sensitivity level. The 3D MTANN is a 3D volume-processing technique based on an artificial neural network which is capable of operating on image data directly. With input CTC volumes and the corresponding teaching volumes, the 3D MTANN can be trained for enhancement of polyps and suppression of non-polyps. We plan to develop a multiple 3D MTANN scheme (multi-3D MTANN) consisting of several expert 3D MTANNs for reduction of various types of false positives including folds, stool, the ileocecal valve, and rectal tubes. By applying a scoring method on the output volumes of the 3D MTANNs, polyp candidates will be classified as polyps or non-polyps. We will compare 3D MTANNs with two-dimensional MTANNs in terms of performance, efficiency, and properties. To obtain reliable evaluation results, we will collect a large database of CTC cases with and without polyps. By comparing with the diagnostic report of the gold standard optical colonoscopy on the same patients, we will determine "missed" cases which are false-negative cases when radiologists read CTC images. We will develop a prototype CAD workstation based on an advanced CAD system incorporating the multi-3D MTANN, and evaluate the performance of the workstation with the database by free-response receiver operating characteristic (FROC) analysis. We plan to carry out an observer performance study to evaluate the potential usefulness of the CAD scheme by use of multi-reader multi-case receiver operating characteristic analysis. The CAD system incorporating with the multi-3D MTANN will provide radiologists with the location of highly suspected lesions, and it has the potential to improve diagnostic accuracy in the early detection of colorectal cancer, which may lead to improved prognosis of patients.